Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Analiza de Sensibilitate cu Analiza Capacității Procesului× | Proiectarea Experimentelor× | |
|---|---|---|
| Domeniu | Design experimental | Design experimental |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1986–2000s (Cp/Cpk indices from Kane 1986; integration formalized in Six Sigma era) | 1935 |
| Autorul original≠ | Synthesized from work by V. E. Kane (process capability indices) and A. Saltelli (sensitivity analysis); integrated in Six Sigma and quality engineering practice | Ronald A. Fisher |
| Tip≠ | Quantitative engineering analysis | Experimental planning framework |
| Sursa seminală≠ | Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). Wiley. ISBN: 978-0470169926 | Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗ |
| Denumiri alternative | Sensitivity-Capability Analysis, PCA with Sensitivity Analysis, Process Capability Sensitivity Study, Cp/Cpk Sensitivity Analysis | DOE, experimental design, factorial experimentation, planned experimentation |
| Înrudite≠ | 5 | 3 |
| Rezumat≠ | Sensitivity analysis with process capability analysis is a quantitative engineering method that combines the measurement of process performance — via capability indices such as Cp and Cpk — with systematic variation of input factors to identify which factors most strongly influence whether a process meets its specification limits. It is widely used in Six Sigma projects, manufacturing quality improvement, and Design of Experiments contexts to prioritize where corrective action will yield the greatest gain in process capability. | Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences. |
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